Diagnosis of functional failures at the circuit board level improves product yield and reduces manufacturing cost. Generally, state-of-the-art board-level diagnostic software is unable to cope with high design complexity and ever-increasing clock frequencies. Some prior art diagnostic systems use brute-force trial-and-error manual debugging. Other diagnostic systems use model-based and rule-based diagnosis limited by knowledge acquisition. Yet other systems using artificial neural network-based inference suffer from theoretical weakness, and limited diagnostic accuracy.
The identification of the root cause of a failure is a major issue. Ambiguous or incorrect repair suggestions lead to long debug times and even to wrong repair actions, which significantly increases the repair cost and adversely impacts yield. To make the matter worse, a board typically has to be scrapped after a repair fails a couple of times. Thus, there is a need for an effective and efficient diagnostic system targeting the above issues.